The earlySTEM Program: An Evaluation Through Teacher Perceptions
Bibliographic record
Abstract
Abstract Conceptually grounded curricular materials in the context of professional development programs facilitate teachers’ adoption of new pedagogies. Even though science, technology, engineering, and mathematics (STEM) professional development opportunities for early grade level teachers continue to receive attention, one existing challenge is to support teachers further in implementing well-defined integrated STEM curricula. The earlySTEM program supports K–4 teachers with the systematically developed earlySTEM curriculum, its associated curricular materials, and year-long mentoring. The program was implemented in 26 schools. This mid-evaluation investigated teacher perceptions of the earlySTEM program with a focus on contributions and challenges. A total of 134 teachers from the 26 schools responded to a survey with open-ended questions. Survey data were analyzed using a descriptive approach. The findings indicated that the teachers had positive experiences with the earlySTEM program. The results revealed that the earlySTEM program is perceived to have contributed to (a) teachers’ STEM teaching skills and STEM conceptualizations and (b) students’ skills development and awareness on the connection of the curriculum content to real-world problems. The results also document the perceived challenge in implementing the earlySTEM curriculum: need for more classroom time. The conclusions offer insights for similar program designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".